SIGNAL PROCESSING METHODS FOR MUSIC TRANSCRIPTION (HB). Questo articolo non è disponibile.
Lingua: inglese
Editore: SP SPRINGER, 2006
- Rilegato
- Nuovo

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Codice articolo CVS 9780387306674
- Titolo
- SIGNAL PROCESSING METHODS FOR MUSIC TRANSCRIPTION (HB)
- Autore
- KLAPURI A.
- Editore
- SP SPRINGER
- Anno di pubblicazione
- 2006
- Condizione
- New
- Rilegatura
- Rilegato
- Lingua
- inglese
- ISBN 10
- 0387306676
- ISBN 13
- 9780387306674
- Edizione
- Edizione Internazionale
Signal Processing Methods for Music Transcription is the first book dedicated to uniting research related to signal processing algorithms and models for various aspects of music transcription such as pitch analysis, rhythm analysis, percussion transcription, source separation, instrument recognition, and music structure analysis. Following a clearly structured pattern, each chapter provides a comprehensive review of the existing methods for a certain subtopic while covering the most important state-of-the-art methods in detail. The concrete algorithms and formulas are clearly defined and can be easily implemented and tested. A number of approaches are covered, including, for example, statistical methods, perceptually-motivated methods, and unsupervised learning methods. The text is enhanced by a common reference and index.
"Riassunto" può appartenere a un’altra edizione di questo titolo.
Dalla quarta di copertina
Signal Processing Methods for Music Transcription is the first book dedicated to uniting research related to signal processing algorithms and models for various aspects of music transcription such as pitch analysis, rhythm analysis, percussion transcription, source separation, instrument recognition, and music structure analysis. Following a clearly structured pattern, each chapter provides a comprehensive review of the existing methods for a certain subtopic while covering the most important state-of-the-art methods in detail. The concrete algorithms and formulas are clearly defined and can be easily implemented and tested. A number of approaches are covered, including, for example, statistical methods, perceptually-motivated methods, and unsupervised learning methods. The text is enhanced by a common reference and index.
This book aims to serve as an ideal starting point for newcomers and an excellent reference source for people already working in the field. Researchers and graduate students in signal processing, computer science, acoustics and music will primarily benefit from this text. It could be used as a textbook for advanced courses in music signal processing. Since it only requires a basic knowledge of signal processing, it is accessible to undergraduate students.
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